An Evolutionary Game-Theoretic Framework for Energy Storage Scheduling in Smart Grids

Abstract

Climate change and the pressing need for sustainable energy solutions have propelled smart grids to the forefront of technological innovation. The emergence of prosumers in smart grids, coupled with the integration of battery energy storage systems (BESS), has significantly enhanced the potential for harnessing renewable energy and optimizing energy consumption patterns. This thesis presents a novel evolutionary game-theoretic (EGT) framework for energy storage scheduling in smart grids, addressing the complex dynamics of local energy markets (LEMs) and the strategic interactions between prosumers equipped with photovoltaic (PV) systems and BESS. At the core of this research is a holistic energy management system (EMS) for prosumers with PV and BESS. This comprehensive framework serves as the foundation for optimizing energy consumption, storage, and trading within a smart grid community (SGC). It considers various factors such as stochastic energy generation, consumption patterns, and fluctuating market conditions to maximize the benefits for individual prosumers and the community as a whole. We propose a novel decentralized EGT approach for exploring a vast action space for battery scheduling over a 24-hour horizon. This innovative algorithm enhances energy management while preserving privacy and scalability. By leveraging EGT, the approach allows for adaptive decision-making in response to changing market conditions and prosumer behaviors, significantly improving upon market stability over traditional scheduling methods. A comprehensive comparative analysis evaluates our proposed EGT algorithm against established methods such as centralized optimization, game theory, and auction-based approaches. This comparison focuses on key performance metrics, including peak-to-average ratio, price volatility, economic efficiency, and computational performance. Results demonstrate the superiority of our EGT approach in terms of both effectiveness and efficiency. The research implements a modified fitness function within the EGT framework that balances technical and economic objectives through the analytic hierarchy process (AHP) approach. This novel fitness evaluation considers both the prosumer's electricity bill and battery degradation, measured by the depth of discharge (DoD). By incorporating these dual objectives, the framework enables prosumers to make informed decisions that balance immediate economic benefits with long-term battery health and sustainability. Building on this enhanced framework, we present a detailed techno-economic analysis that evaluates the financial viability of BESS investments within the SGC. Through considering metrics such as the net present value (NPV) and payback periods, we provide valuable insights into the economic implications of different battery utilization strategies. Our analysis reveals how varying the weights between economic and technical objectives impacts both immediate economic returns and long-term system sustainability. By incorporating these economic metrics alongside technical considerations in our EGT framework, we achieve a balanced approach that addresses both financial viability and system longevity. Extensive simulations demonstrate the effectiveness of our EGT algorithm in stabilizing market conditions under various Feed-in Tariff (FiT) scenarios and prosumer penetration rates. The results show how increased prosumer participation leads to more predictable investment outcomes and stable market conditions. This thesis provides an in-depth analysis of a demand-side management scheme that can be employed by prosumers worldwide in the near future, contributing to the global effort in combating climate change through advanced energy management solutions

Date of Award	2024
Original language	American English
Awarding Institution	HBKU College of Science and Engineering